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Phospho-Proteomics pp 323–339Cite as

Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1355))

Abstract

Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. Several platforms for such automated interpretation of mass spectrometric data have been developed, each having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.

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Acknowledgments

This work was in part funded by the Novo Nordisk Foundation Center for Protein Research [NNF14CC0001]

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Authors and Affiliations

  1. Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark

    Jan C. Refsgaard & Stephanie Munk

  2. Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, Bldg. 6.2, 2200, Copenhagen, Denmark

    Jan C. Refsgaard & Lars J. Jensen

Authors
  1. Jan C. Refsgaard
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  2. Stephanie Munk
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  3. Lars J. Jensen
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Corresponding author

Correspondence to Lars J. Jensen .

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Editors and Affiliations

  1. University of Copenhagen, Center for Protein Research, Copenhagen Nm, Denmark

    Louise von Stechow

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Refsgaard, J.C., Munk, S., Jensen, L.J. (2016). Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics. In: von Stechow, L. (eds) Phospho-Proteomics. Methods in Molecular Biology, vol 1355. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3049-4_22

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  • DOI: https://doi.org/10.1007/978-1-4939-3049-4_22

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-3048-7

  • Online ISBN: 978-1-4939-3049-4

  • eBook Packages: Springer Protocols

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